removed the is_mme in the code

Change-Id: I657911875c7904dfa79a84f80d6f23778d77c812
Signed-off-by: rajdeep11 <rajdeep.sin@samsung.com>

diff --git a/tests/test_tm_apis.py b/tests/test_tm_apis.py
index 320f7f0..8aa4d71 100644 (file)
--- a/tests/test_tm_apis.py
+++ b/tests/test_tm_apis.py
@@ -282,7 +282,6 @@ class Test_get_trainingjob_by_name_version:
         creation_time = datetime.datetime.now()
         updation_time = datetime.datetime.now()
         training_config = {
-            "is_mme" : True,
             "description": "Test description",
             "dataPipeline": {
                 "feature_group_name": "test_feature_group",
@@ -309,9 +308,7 @@ class Test_get_trainingjob_by_name_version:
             version=1,
             model_url="http://test.model.url",
             notification_url="http://test.notification.url",
-            deletion_in_progress=False,
-            model_name="test_model",
-            model_info="test_model_info"
+            deletion_in_progress=False
         )
 
     @pytest.fixture
@@ -341,9 +338,6 @@ class Test_get_trainingjob_by_name_version:
         assert job_data['training_config']['description'] == "Test description"
         assert job_data['training_config']['dataPipeline']['feature_group_name'] == "test_feature_group"
         assert job_data['training_config']['trainingPipeline']['pipeline_name'] == "test_pipeline"
-        assert job_data['training_config']['is_mme'] is True
-        assert job_data['model_name'] == "test_model"
-        assert job_data['model_info'] == "test_model_info"
         assert job_data['accuracy'] == mock_metrics
 
     @patch('trainingmgr.trainingmgr_main.check_trainingjob_name_and_version', return_value=False)

diff --git a/tests/test_trainingmgr_util.py b/tests/test_trainingmgr_util.py
index 85002c6..aa404bc 100644 (file)
--- a/tests/test_trainingmgr_util.py
+++ b/tests/test_trainingmgr_util.py
@@ -272,9 +272,9 @@ class Test_check_trainingjob_data:
     @patch('trainingmgr.common.trainingmgr_util.isinstance',return_value=True)  
     def test_check_trainingjob_data(self,mock1,mock2):
         usecase_name = "usecase8"
-        json_data = { "description":"unittest", "featureGroup_name": "group1" , "pipeline_name":"qoe" , "experiment_name":"experiment1" , "arguments":"arguments1" , "query_filter":"query1" , "enable_versioning":True , "target_deployment":"Near RT RIC" , "pipeline_version":1 , "datalake_source":"cassandra db" , "incremental_training":True , "model":"usecase7" , "model_version":1 , "is_mme":False, "model_name":""}
+        json_data = { "description":"unittest", "featureGroup_name": "group1" , "pipeline_name":"qoe" , "experiment_name":"experiment1" , "arguments":"arguments1" , "query_filter":"query1" , "enable_versioning":True , "target_deployment":"Near RT RIC" , "pipeline_version":1 , "datalake_source":"cassandra db" , "incremental_training":True , "model":"usecase7" , "model_version":1 }
     
-        expected_data = ("group1", 'unittest', 'qoe', 'experiment1', 'arguments1', 'query1', True, 1, 'cassandra db',False, "")
+        expected_data = ("group1", 'unittest', 'qoe', 'experiment1', 'arguments1', 'query1', True, 1, 'cassandra db')
         assert check_trainingjob_data(usecase_name, json_data) == expected_data,"data not equal"
     
     def test_negative_check_trainingjob_data_1(self):

diff --git a/trainingmgr/common/trainingConfig_parser.py b/trainingmgr/common/trainingConfig_parser.py
index 9467c67..b3155fc 100644 (file)
--- a/trainingmgr/common/trainingConfig_parser.py
+++ b/trainingmgr/common/trainingConfig_parser.py
@@ -40,7 +40,6 @@ def __getLeafPaths():
         It returns paths possible to retrieve data
         Based on TrainingConfig Schema:
         {
-            "is_mme" : false,
             "description": "This is something3",
             "dataPipeline": {
                 "feature_group_name": "base2",
@@ -54,7 +53,6 @@ def __getLeafPaths():
         }
     '''
     paths = {
-        "is_mme": ["is_mme"],
         "description": ["description"],
         "feature_group_name": ["dataPipeline", "feature_group_name"],
         "query_filter" : ["dataPipeline", "query_filter"],

diff --git a/trainingmgr/common/trainingmgr_util.py b/trainingmgr/common/trainingmgr_util.py
index f63854f..083ccd7 100644 (file)
--- a/trainingmgr/common/trainingmgr_util.py
+++ b/trainingmgr/common/trainingmgr_util.py
@@ -139,8 +139,7 @@ def check_trainingjob_data(trainingjob_name, json_data):
                                  "pipeline_name", "experiment_name",
                                  "arguments", "enable_versioning",
                                  "datalake_source", "description",
-                                 "query_filter",
-                                 "is_mme", "model_name"], json_data):
+                                 "query_filter"], json_data):
 
             description = json_data["description"]
             feature_list = json_data["featureGroup_name"]
@@ -154,8 +153,6 @@ def check_trainingjob_data(trainingjob_name, json_data):
             enable_versioning = json_data["enable_versioning"]
             pipeline_version = json_data["pipeline_version"]
             datalake_source = json_data["datalake_source"]
-            is_mme=json_data["is_mme"]
-            model_name=json_data["model_name"]
         else :
             raise TMException("check_trainingjob_data- supplied data doesn't have" + \
                                 "all the required fields ")
@@ -164,7 +161,7 @@ def check_trainingjob_data(trainingjob_name, json_data):
                            str(err)) from None
     return (feature_list, description, pipeline_name, experiment_name,
             arguments, query_filter, enable_versioning, pipeline_version,
-            datalake_source, is_mme, model_name)
+            datalake_source)
 
 def check_feature_group_data(json_data):
     """

diff --git a/trainingmgr/models/trainingjob.py b/trainingmgr/models/trainingjob.py
index d12e6fe..4c6eb33 100644 (file)
--- a/trainingmgr/models/trainingjob.py
+++ b/trainingmgr/models/trainingjob.py
@@ -52,9 +52,7 @@ class TrainingJob(db.Model):
     training_config = Column(String(5000), nullable=False)
     model_url = Column(String(1000), nullable=True)
     notification_url = Column(String(1000), nullable=True)
-    model_name = db.Column(db.String(128), nullable=True)
     model_id = Column(Integer, nullable=False)
-    model_info = Column(String(1000), nullable=True)
 
     #defineing relationships
     steps_state = relationship("TrainingJobStatus", back_populates="trainingjobs")

diff --git a/trainingmgr/trainingmgr_main.py b/trainingmgr/trainingmgr_main.py
index 8251f4a..dbf2e4c 100644 (file)
--- a/trainingmgr/trainingmgr_main.py
+++ b/trainingmgr/trainingmgr_main.py
@@ -146,8 +146,6 @@ def get_trainingjob_by_name_version(trainingjob_name, version):
                              url for downloading model
                         notification_url: str
                              url of notification server
-                        is_mme: boolean
-                            whether the mme is enabled
                         model_name: str
                             model name 
                         model_info: str
@@ -191,9 +189,6 @@ def get_trainingjob_by_name_version(trainingjob_name, version):
                 # "datalake_source": get_one_key(json.loads(trainingjob.datalake_source)['datalake_source']),
                 "model_url": trainingjob.model_url,
                 "notification_url": trainingjob.notification_url,
-                # "is_mme": trainingjob.is_mme, 
-                "model_name": trainingjob.model_name,
-                "model_info": trainingjob.model_info,
                 "accuracy": data
             }
             response_data = {"trainingjob": dict_data}
@@ -605,19 +600,6 @@ def pipeline_notification():
                                                         States.FINISHED.name)
                 notification_rapp(trainingjob_info, TRAININGMGR_CONFIG_OBJ)
                 # upload to the mme
-                is_mme = getField(trainingjob_info.training_config, "is_mme")
-                if is_mme:   
-                    model_name=trainingjob_info.model_name #model_name
-                    file=MM_SDK.get_model_zip(trainingjob_name, str(version))
-                    url ="http://"+str(TRAININGMGR_CONFIG_OBJ.model_management_service_ip)+":"+str(TRAININGMGR_CONFIG_OBJ.model_management_service_port)+"/uploadModel/{}".format(model_name)
-                    LOGGER.debug("url for upload is: ", url)
-                    resp2=requests.post(url=url, files={"file":('Model.zip', file, 'application/zip')})
-                    if resp2.status_code != status.HTTP_200_OK :
-                        errMsg= "Upload to mme failed"
-                        LOGGER.error(errMsg + trainingjob_name)
-                        raise TMException(errMsg + trainingjob_name)
-                    
-                    LOGGER.debug("Model uploaded to the MME")
             else:
                 errMsg = "Trained model is not available  "
                 LOGGER.error(errMsg + trainingjob_name)
@@ -945,8 +927,6 @@ def trainingjob_operations(trainingjob_name):
                     Name of model
                 trainingConfig: dict
                     Training-Configurations, parameter as follows
-                    is_mme: boolean
-                        whether mme is enabled
                     description: str
                         description
                     dataPipeline: dict
@@ -1006,26 +986,6 @@ def trainingjob_operations(trainingjob_name):
                 processed_json_data = request.get_json()
                 processed_json_data['training_config'] = json.dumps(request.get_json()["training_config"])
                 trainingjob = trainingjob_schema.load(processed_json_data)
-                model_info=""
-                if  getField(trainingjob.training_config, "is_mme"):
-                    pipeline_dict =json.loads(TRAININGMGR_CONFIG_OBJ.pipeline)
-                    model_info=get_model_info(TRAININGMGR_CONFIG_OBJ, trainingjob.model_name)
-                    s=model_info["meta-info"]["feature-list"]
-                    model_type=model_info["meta-info"]["model-type"]
-                    try:
-                        pipeline_name=pipeline_dict[str(model_type)]
-                    except Exception as err:
-                        err="Doesn't support the model type"
-                        raise TMException(err)
-                    pipeline_version=pipeline_name
-                    feature_list=','.join(s)
-                    result= get_feature_groups_db(PS_DB_OBJ)
-                    for res in result:
-                        if feature_list==res[1]:
-                            featuregroup_name=res[0]
-                            break 
-                    if featuregroup_name =="":
-                        return {"Exception":"The no feature group with mentioned feature list, create a feature group"}, status.HTTP_406_NOT_ACCEPTABLE
                 add_update_trainingjob(trainingjob, True)
                 api_response =  {"result": "Information stored in database."}                 
                 response_code = status.HTTP_201_CREATED